From Model Drift to Behavioral Drift: Why AI Failures Are Governance Failures

This whitepaper reframes AI drift as a governance failure rather than a purely technical phenomenon. While model drift traditionally refers to statistical changes in data or performance, many real-world AI failures occur despite stable models and nominal metrics. These failures emerge instead from behavioral drift: gradual changes in how systems are used, relied upon, and interpreted over time without corresponding governance controls. The paper introduces behavioral drift as a longitudinal governance signal and explains how weak authority structures, absent escalation paths, and non-durable evidence allow unethical or unsafe operation to normalize before technical alarms trigger. This work is intended as a governance reference for enterprise, audit, and policy audiences and uses canonical terminology defined in Canonical Terms v1.0.0 (Zenodo DOI: https://doi.org/10.5281/zenodo.18615600�).